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To generate synthetic time-series data without faking the signal, decide which patterns matter for your use, choose a generator suited to those patterns, and compare its output with the real series. Check more than value ranges: temporal dependence, trend or seasonality, relationships between variables, domain rules, and the variety of generated sequences may all matter.
What does it mean to preserve a time-series signal?
A time series is more than a collection of values. The order of observations can carry information: trends, seasonality, periodicity, and dependencies between nearby time points may be essential to the task. Shuffling observations or matching only a histogram can preserve the range of values while destroying that temporal structure. YData’s time-series overview describes these sequential characteristics as central to the data type.
“Preserve the signal” therefore does not mean making every synthetic sequence identical to a real one. It means retaining the properties that make the data useful for its intended purpose, while producing plausible variation. A series for testing a plotting routine may need different checks from one used to train a forecasting model or shared outside an organization.
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There is no universally best generator. The options range from constructing signals from specified processes to transforming existing sequences or fitting learned models. The right choice depends on the structure you need, the constraints you must respect, and whether you have representative real data.
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| Method family | When it may fit | What to watch | Examples and documentation |
|---|---|---|---|
| Signal and stochastic-process construction | You can describe the mechanisms to simulate, or need controlled scenarios for testing. | The output reflects the assumptions you specify; a realistic-looking process can still be a poor model of the target system. | TimeSynth documents harmonic signals, Gaussian processes with several kernels, pseudoperiodic signals, autoregressive and continuous autoregressive processes, and NARMA. |
| Resampling and transformations | You have example sequences and want controlled variations around them. | Each transformation changes some properties. Check that the changes do not break timing, shape, scale, or other invariants your task depends on. | TSGM documentation describes options including moving-block bootstrap, jittering, scaling, magnitude warping, time warping, and DTW barycentric averaging. |
| Learned sequential generators | You have representative sequences and want a model to learn patterns from data. | Fit and evaluate on data representative of the target use. Documentation describes intended capabilities, not a guarantee that a model preserves the signal in your dataset. | YData SDK’s TimeSeriesSynthesizer documents learning sequence patterns and relationships over time. YData-Synthetic’s version 1.3 TimeGAN documentation describes a GAN-based option for temporal data. |
| Frequency-domain surrogate generation | You want to generate surrogates with controllable similarity to a reference signal. | A reported preservation result applies to the specific method and properties studied; it does not establish preservation of every temporal feature or nonstationary pattern. | A 2025 preprint by Ofek Aloni, Gal Perelman, and Barak Fishbain proposes a discrete-Fourier-transform method and reports analytical preservation of the first two statistical moments and autocorrelation: the paper on arXiv. |
When several approaches seem plausible, compare whether your use requires exact timing or irregular sampling, which trends and cross-series relationships matter, whether rare patterns must appear, and how much source data and computation you can use. Also consider whether performance on a downstream task transfers from synthetic data to real data. The cited sources do not establish a universal ranking or threshold for these choices.
Define what must stay true before generating data
Write down the intended use first: code testing, simulation, data augmentation, forecasting or classification experiments, or data sharing. Then translate that purpose into checks. This avoids treating a visually convincing plot as proof that the synthetic series is fit for purpose.
- Values: Note plausible ranges and the marginal distributions that matter.
- Time structure: Identify relevant autocorrelation lags, trends, seasonal patterns, and periodic behavior.
- Relationships: For multivariate or per-entity data, note which features or series should move together or remain distinct.
- Data conditions: Include missingness patterns, sampling intervals, and domain rules such as valid bounds or event ordering.
- Coverage: Decide which regimes, unusual events, and rare patterns should be represented, rather than expecting every sequence to resemble the average case.
This checklist is a practical way to apply the time-series properties described in YData’s overview and the UK Office for National Statistics’ guidance to prioritize statistical properties that matter to users. It is not a universal checklist supplied by either source.
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Generate multiple sequences, then compare them with a suitable reference using the checks chosen above. Keep comparisons matched: use the same time window, variables, aggregation, and relevant population or entities where possible. The UK Office for National Statistics (ONS) describes visual comparison and differences in estimates as simple evaluation options, with more complex modelling potentially needed for multivariate properties. Its synthetic data pilot methodology emphasizes that evaluation depends on user needs, disclosure concerns, and available resources.
- Compare values and ranges. Check whether synthetic values obey domain bounds and whether the important distributions resemble the reference closely enough for the stated purpose.
- Compare temporal patterns. Inspect plots over time and compare the trend, seasonality, and autocorrelation at the lags you identified. Similar overall ranges do not establish that these patterns survived.
- Check relationships and constraints. For data with multiple variables or entities, examine the relationships you identified and test domain rules, including missingness or event-order requirements.
- Inspect variation across generated sequences. Look for near-copies, missing regimes, and implausible combinations. A generator can produce plausible individual sequences yet cover too narrow a range of patterns; the 2021 MethodsX article, “A Methodology for Validating Diversity in Synthetic Time Series Generation,” treats dataset-level diversity as a distinct validation concern.
- Test the intended task. If the data is meant to train or evaluate a model, test whether the result is useful for that task rather than relying on resemblance alone. The appropriate utility test depends on the use; the cited ONS guidance does not prescribe one universal test.
- Assess disclosure separately before release. Similarity and utility checks do not determine whether synthetic records reveal sensitive information. ONS describes disclosure assessment as case-specific and notes that plausible values can still be disclosive in longitudinal data.
Do not turn a single passing check into a general claim that the signal is preserved. A generator might match value distributions but miss temporal dependence, or reproduce common patterns while omitting important rare ones. Record which properties you checked and what the results support.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to interpret published method claims
Published tools and methods can help narrow the options, but their documented capabilities are not evidence that a generator will work on every dataset. For example, the 2025 Fourier-method preprint reports analytical preservation of two moments and autocorrelation for its proposed method; that finding should not be extended to every temporal feature or to all nonstationary series. Likewise, a model’s documentation describing learned sequence relationships is not a substitute for checking those relationships in your own output.
Keep claims proportionate to the validation performed: say which properties were compared, on what data and for what use, and distinguish observed similarity from downstream utility or disclosure safety. No single fidelity score or visual check resolves all three questions.
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